Due to the common character of pixel-level prediction among different tasks, some work paid attention to studying joint learning. In [1]}, Wang et al. used a network to jointly predict a depth map and a semantic probability map. The network, however, is limited by only the last layer being changed for prediction. Jiao et al. [2]} proposed a network with a backbone encoder and two sub-networks as decoders for respective prediction, increasing both the accuracy of depth estimation and semantic segmentation dramatically. Later, PAD-Net was proposed by Xu et al. [3]}, which used four intermediate auxiliary tasks, providing abundant information for prediction. In the recent research, the SOSD-Net [4]} made full use of the geometric relations between depth estimation and semantic segmentation to predict. Although these works achieved outstanding performance, they didn't exploit the feature that semantic labels could help network capture prior contextual knowledge of the scene to improve the accuracy of prediction.
